1. Field of the Invention
The present invention generally relates to the field of computer image processing of computer tomography (CT) data and, more particularly, to reducing artifacts in computed tomography images arising from the process of reconstructing the images from projections.
2. Background Description
U.S. Pat. No. 5,416,815, which is incorporated here by reference in its entirety, describes computer tomography (CT) systems and the method of image reconstruction from projections. X-ray CT images of objects containing metal are often corrupted by noise in the form of blooming and streaking artifacts that radiate from the regions of the image where the metal is present. These artifacts are referred to as "metal-induced reconstruction" artifacts, and the process of reducing their effect as "metal artifact reduction" (or MAR). Metal-induced artifacts severely limit the clinical usefulness of CT images, both for diagnostic and therapeutic purposes.
The prior art methods of MAR fall into two groups. In the first group are image processing methods which have been applied directly to the noisy CT images. See, for example, D. D. Robertson, P. J. Weiss, E. K. Fishman, D. Magid, and P. S. Walker, "Evaluation of CT techniques for reducing artifacts in the presence of metallic orthopedic implants", Journal of Computer Assisted Tomography, March-April 1988, 12(2), pp. 236-41; Hamid Soltanian-Zadeh, Joe P. Windham, and Jalal Soltanianzadeh, "CT Artifact Correction: An Image Processing Approach", SPIE Medical Imaging '96, Newport Beach, Calif., February 1996; and Heang K. Tuy, "An Algorithm to Reduce Clip Artifacts in CT Images", SPIE Vol. 1652 Medical Imaging VI: Image Processing (1992).
Methods which apply image processing directly, process the corrupted CT images data only. These methods do not make use of any projection data. This approach is limited by the fact that essential image information is completely erased by MAR artifacts. This information cannot be recovered solely from the corrupted images themselves. Therefore, these methods are unable to recover this information. Further, they do not directly address the problem of recovering quantitative boundary information, but rather they focus on improving the overall qualitative appearance of the images.
With the second group of methods, the projection data are processed directly (typically by filling in "missing" data), and the images reconstructed from these modified projections. See, for example, G. H. Glover and N. J. Pelc, "An algorithm for the reduction of metal clip artifacts in CT reconstructions", Medical Physics, 8(6), November/December 1981, pp. 799-807; T. Hinderling, P. Ruegsegger, M. Anliker, and C. Dietschi, "Computed Tomography reconstruction from hollow projections: an application to in vivo evaluation of artificial hip joints", Journal of Computer Assisted Tomography, February 1979, 3(1), pp. 52-57; W. A. Kalender, R. Hebel, and J. Ebersberger, "Reduction of CT artifacts caused by metallic implants", Radiology, August 1987, 164(2), pp. 57-7; E. Klotz, W. A. Kalender, R. Sokiranski, and D. Felsenberg, "Algorithms for the reduction of CT artifacts caused by metallic implants", Medical Imaging IV: PACS System Design and Evaluation, vol. 1234, Newport Beach, Calif., February 1990, pp. 642-650; R. M. Lewitt and R. H. T. Bates, "Image reconstruction from projections: IV: Projection completion methods (computational examples)", Optik 50, 1978, pp. 269-278; B. E. Oppenheim, "Reconstruction tomography from incomplete projections", Reconstruction Tomography in Diagnostic and Nuclear Medicine, Ter-Pogossian (editor), University Park Press, Baltimore, 1977, pp. 155-183; and G. Wang, D. L. Snyder, A. O'Sullivan, and M. W. Vannier, "Iterative deblurring for CT metal artifact reduction", IEEE Trans. Medical Imaging, October 1996, 14(5), pp. 657-664.
Methods which process projection data directly, process the projection data only. They do not make use of the CT image data. Further, a major deficiency of these methods that they work only with a very specific type of projection data; that is, projection data that (i) have been highly-sampled, and (ii) are of high resolution. The methods will fail if applied to sparsely-sampled or low-resolution projection data.